from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image, StableDiffusionPipeline, EulerDiscreteScheduler import torch import os try: import intel_extension_for_pytorch as ipex except: pass from PIL import Image import numpy as np import gradio as gr import psutil import time import math SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None) TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) HF_TOKEN = os.environ.get("HF_TOKEN", None) # check if MPS is available OSX only M1/M2/M3 chips mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available() device = torch.device( "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu" ) torch_device = device torch_dtype = torch.float32 #float16 print(f"SAFETY_CHECKER: {SAFETY_CHECKER}") print(f"TORCH_COMPILE: {TORCH_COMPILE}") print(f"device: {device}") if mps_available: device = torch.device("mps") torch_device = "cpu" torch_dtype = torch.float32 repo_id = "runwayml/stable-diffusion-v1-5" scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler") t2i_pipe = StableDiffusionPipeline.from_single_file( "https://huggingface.co/wanghuging/skin_demo/blob/main/skin_demo.safetensors", scheduler=scheduler, safety_checker = None, requires_safety_checker = False ) # if SAFETY_CHECKER == "True": # i2i_pipe = AutoPipelineForImage2Image.from_pretrained( # "stabilityai/sdxl-turbo", # torch_dtype=torch_dtype, # variant="fp16" if torch_dtype == torch.float16 else "fp32", # ) # t2i_pipe = AutoPipelineForText2Image.from_pretrained( # #"stabilityai/sdxl-turbo", # # "wanghuging/demo_model", # #"stabilityai/stable-diffusion-xl-base-1.0", # "stabilityai/stable-diffusion-2-1", # torch_dtype=torch_dtype, # variant="fp16" #if torch_dtype == torch.float16 else "fp32", # ) # else: # i2i_pipe = AutoPipelineForImage2Image.from_pretrained( # "stabilityai/sdxl-turbo", # safety_checker=None, # torch_dtype=torch_dtype, # variant="fp16" if torch_dtype == torch.float16 else "fp32", # ) # t2i_pipe = AutoPipelineForText2Image.from_pretrained( # #"stabilityai/sdxl-turbo", # # "wanghuging/demo_model", # # "stabilityai/stable-diffusion-xl-base-1.0", # "stabilityai/stable-diffusion-2-1", # safety_checker=None, # torch_dtype=torch_dtype, # variant="fp16" #if torch_dtype == torch.float16 else "fp32", # ) # t2i_pipe.load_lora_weights("wanghuging/skin_demo", weight_name="skin_demo.safetensors") t2i_pipe.safety_checker = lambda images, clip_input: (images, False) t2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) t2i_pipe.set_progress_bar_config(disable=True) # i2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) # i2i_pipe.set_progress_bar_config(disable=True) def resize_crop(image, size=512): image = image.convert("RGB") w, h = image.size image = image.resize((size, int(size * (h / w))), Image.BICUBIC) return image # async def predict(init_image, prompt, strength, steps, seed=1231231): # # init_image = None # if init_image is not None: # init_image = resize_crop(init_image) # generator = torch.manual_seed(seed) # last_time = time.time() # if int(steps * strength) < 1: # steps = math.ceil(1 / max(0.10, strength)) # results = i2i_pipe( # prompt=prompt, # image=init_image, # generator=generator, # num_inference_steps=steps, # guidance_scale=0.0, # strength=strength, # width=512, # height=512, # output_type="pil", # ) # else: # generator = torch.manual_seed(seed) # last_time = time.time() # t2i_pipe.safety_checker = None # t2i_pipe.requires_safety_checker = False # results = t2i_pipe( # prompt=prompt, # generator=generator, # num_inference_steps=steps, # guidance_scale=0.0, # width=512, # height=512, # output_type="pil", # ) # print(f"Pipe took {time.time() - last_time} seconds") # nsfw_content_detected = ( # results.nsfw_content_detected[0] # if "nsfw_content_detected" in results # else False # ) # if nsfw_content_detected: # gr.Warning("NSFW content detected.") # return Image.new("RGB", (512, 512)) # return results.images[0] async def predict(prompt, neg_prompt, strength, steps, seed=1231231): generator = torch.manual_seed(seed) last_time = time.time() t2i_pipe.safety_checker = None t2i_pipe.requires_safety_checker = False results = t2i_pipe( prompt=prompt, negative_prompt = neg_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0, width=512, height=512, output_type="pil", ) print(f"Pipe took {time.time() - last_time} seconds") nsfw_content_detected = ( results.nsfw_content_detected[0] if "nsfw_content_detected" in results else False ) if nsfw_content_detected: gr.Warning("NSFW content detected.") return Image.new("RGB", (512, 512)) return results.images[0] css = """ #container{ margin: 0 auto; max-width: 80rem; } #intro{ max-width: 100%; text-align: center; margin: 0 auto; } """ with gr.Blocks(css=css) as demo: # init_image_state = gr.State() with gr.Column(elem_id="container"): gr.Markdown( """# Derm-T2IM Text to Image Skin Cancer ## Demo **Model**: https://huggingface.co/wanghuging/skin_demo """, elem_id="intro", ) with gr.Row(): prompt = gr.Textbox( placeholder="Insert your prompt here:", scale=5, container=False, ) # neg_prompt = gr.Textbox( # placeholder="Insert your negative prompt here:", # scale=5, # container=False, # ) generate_bt = gr.Button("Generate", scale=1) with gr.Row(): neg_prompt = gr.Textbox( placeholder="Insert your negative prompt here:", scale=5, container=False, ) with gr.Row(): # with gr.Column(): # neg_prompt = gr.Textbox( # placeholder="Insert your negative prompt here:", # scale=5, # container=False, # ) # with gr.Column(): # image_input = gr.Image( # sources=["upload", "webcam", "clipboard"], # label="Webcam", # type="pil", # ) with gr.Column(): image = gr.Image(type="filepath") with gr.Column(): with gr.Accordion("Advanced options", open=False): strength = gr.Slider( label="Strength", value=0.7, minimum=0.0, maximum=1.0, step=0.001, ) steps = gr.Slider( label="Steps", value=2, minimum=1, maximum=25, step=1 ) seed = gr.Slider( randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1, ) # with gr.Accordion("Run with diffusers"): # gr.Markdown( # """## Running SDXL Turbo with `diffusers` # ```bash # pip install diffusers==0.23.1 # ``` # ```py # from diffusers import DiffusionPipeline # pipe = DiffusionPipeline.from_pretrained( # "stabilityai/sdxl-turbo" # ).to("cuda") # results = pipe( # prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe", # num_inference_steps=1, # guidance_scale=0.0, # ) # imga = results.images[0] # imga.save("image.png") # ``` # """ # ) inputs = [prompt, neg_prompt, strength, steps, seed] # inputs = [image_input, prompt, strength, steps, seed] generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False) prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) neg_prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) strength.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) # image_input.change( # fn=lambda x: x, # inputs=image_input, # outputs=init_image_state, # show_progress=False, # queue=False, # ) demo.queue() demo.launch()